Overview

Dataset statistics

Number of variables10
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory132.9 KiB
Average record size in memory136.1 B

Variable types

Numeric7
Categorical3

Alerts

Batch_Failure is highly overall correlated with Enzyme_Activity_UmLHigh correlation
Biomass_OD600 is highly overall correlated with Time_hrHigh correlation
Enzyme_Activity_UmL is highly overall correlated with Batch_FailureHigh correlation
Time_hr is highly overall correlated with Biomass_OD600High correlation
Batch_Failure is highly imbalanced (90.0%)Imbalance
Time_hr is uniformly distributedUniform
Batch_ID is uniformly distributedUniform
Temperature_C has unique valuesUnique
pH has unique valuesUnique
DO_percent has unique valuesUnique
Glucose_gL has unique valuesUnique
Biomass_OD600 has unique valuesUnique
Enzyme_Activity_UmL has unique valuesUnique

Reproduction

Analysis started2026-02-10 09:14:10.694996
Analysis finished2026-02-10 09:14:16.273836
Duration5.58 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Time_hr
Real number (ℝ)

High correlation  Uniform 

Distinct100
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.5
Minimum0
Maximum99
Zeros10
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-10T14:44:16.479934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.95
Q124.75
median49.5
Q374.25
95-th percentile94.05
Maximum99
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation28.880514
Coefficient of variation (CV)0.58344473
Kurtosis-1.2002388
Mean49.5
Median Absolute Deviation (MAD)25
Skewness0
Sum49500
Variance834.08408
MonotonicityNot monotonic
2026-02-10T14:44:16.594814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010
 
1.0%
110
 
1.0%
210
 
1.0%
310
 
1.0%
410
 
1.0%
510
 
1.0%
610
 
1.0%
710
 
1.0%
810
 
1.0%
910
 
1.0%
Other values (90)900
90.0%
ValueCountFrequency (%)
010
1.0%
110
1.0%
210
1.0%
310
1.0%
410
1.0%
510
1.0%
610
1.0%
710
1.0%
810
1.0%
910
1.0%
ValueCountFrequency (%)
9910
1.0%
9810
1.0%
9710
1.0%
9610
1.0%
9510
1.0%
9410
1.0%
9310
1.0%
9210
1.0%
9110
1.0%
9010
1.0%

Temperature_C
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.028998
Minimum32.138099
Maximum42.779097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-10T14:44:16.714819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32.138099
5-th percentile34.711068
Q136.028615
median37.037951
Q337.971916
95-th percentile39.515459
Maximum42.779097
Range10.640998
Interquartile range (IQR)1.9433013

Descriptive statistics

Standard deviation1.4688239
Coefficient of variation (CV)0.039666855
Kurtosis0.072562202
Mean37.028998
Median Absolute Deviation (MAD)0.96927905
Skewness0.11697637
Sum37028.998
Variance2.1574437
MonotonicityNot monotonic
2026-02-10T14:44:16.834911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.745071231
 
0.1%
36.792603551
 
0.1%
37.971532811
 
0.1%
39.284544781
 
0.1%
36.648769941
 
0.1%
36.648794561
 
0.1%
39.368819221
 
0.1%
38.151152091
 
0.1%
36.295788421
 
0.1%
37.813840071
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
32.138098991
0.1%
32.954670041
0.1%
33.023545291
0.1%
33.070382341
0.1%
33.292533251
0.1%
33.364181011
0.1%
33.547118251
0.1%
33.683297041
0.1%
33.701791071
0.1%
33.814156411
0.1%
ValueCountFrequency (%)
42.779097241
0.1%
41.618321211
0.1%
41.080253751
0.1%
40.94857311
0.1%
40.86003971
0.1%
40.840126811
0.1%
40.790398641
0.1%
40.694863171
0.1%
40.682950211
0.1%
40.668627971
0.1%

pH
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8141672
Minimum6.2119223
Maximum7.4386215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-10T14:44:16.951218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.2119223
5-th percentile6.4846736
Q16.6787517
median6.8126154
Q36.9457764
95-th percentile7.1390818
Maximum7.4386215
Range1.2266992
Interquartile range (IQR)0.26702477

Descriptive statistics

Standard deviation0.19949088
Coefficient of variation (CV)0.029275899
Kurtosis0.058403159
Mean6.8141672
Median Absolute Deviation (MAD)0.1338355
Skewness-0.049395899
Sum6814.1672
Variance0.039796609
MonotonicityNot monotonic
2026-02-10T14:44:17.067087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0798710871
 
0.1%
6.9849267371
 
0.1%
6.8119260741
 
0.1%
6.6706126441
 
0.1%
6.9396446631
 
0.1%
6.8786970771
 
0.1%
6.9790386441
 
0.1%
6.927034361
 
0.1%
7.0099105431
 
0.1%
6.6929529581
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
6.2119222731
0.1%
6.2157299031
0.1%
6.2207489241
0.1%
6.2255475571
0.1%
6.2302914761
0.1%
6.2593535411
0.1%
6.2817915421
0.1%
6.2892157731
0.1%
6.2939424951
0.1%
6.3001188571
0.1%
ValueCountFrequency (%)
7.4386215141
0.1%
7.4275497071
0.1%
7.3288686691
0.1%
7.3203366231
0.1%
7.3179127281
0.1%
7.3159418681
0.1%
7.3116398571
0.1%
7.2985999031
0.1%
7.2879504811
0.1%
7.2806831171
0.1%

DO_percent
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.058342
Minimum-2.4750484
Maximum76.099186
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2026-02-10T14:44:17.186403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.4750484
5-th percentile26.232787
Q137.175279
median44.425576
Q351.266056
95-th percentile60.823743
Maximum76.099186
Range78.574234
Interquartile range (IQR)14.090776

Descriptive statistics

Standard deviation10.928108
Coefficient of variation (CV)0.2480372
Kurtosis0.82720076
Mean44.058342
Median Absolute Deviation (MAD)7.0823559
Skewness-0.38623898
Sum44058.342
Variance119.42354
MonotonicityNot monotonic
2026-02-10T14:44:17.304449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.248217251
 
0.1%
43.554813291
 
0.1%
37.075800791
 
0.1%
41.92038471
 
0.1%
26.063853331
 
0.1%
47.132937071
 
0.1%
45.012054751
 
0.1%
36.829113691
 
0.1%
51.592456681
 
0.1%
54.375701381
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-2.4750484271
0.1%
2.6176876671
0.1%
3.6943352021
0.1%
3.7929984741
0.1%
4.1297298141
0.1%
4.1588706051
0.1%
13.951372071
0.1%
13.97707851
0.1%
14.804878441
0.1%
15.088640291
0.1%
ValueCountFrequency (%)
76.099185561
0.1%
74.852590031
0.1%
74.490944251
0.1%
72.596600391
0.1%
71.930336641
0.1%
71.207930911
0.1%
69.267164861
0.1%
69.126154221
0.1%
69.116766761
0.1%
68.638724951
0.1%

Agitation_RPM
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
400
352 
300
342 
200
306 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row300
2nd row300
3rd row300
4th row200
5th row400

Common Values

ValueCountFrequency (%)
400352
35.2%
300342
34.2%
200306
30.6%

Length

2026-02-10T14:44:17.417334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-10T14:44:17.505014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
400352
35.2%
300342
34.2%
200306
30.6%

Most occurring characters

ValueCountFrequency (%)
02000
66.7%
4352
 
11.7%
3342
 
11.4%
2306
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02000
66.7%
4352
 
11.7%
3342
 
11.4%
2306
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02000
66.7%
4352
 
11.7%
3342
 
11.4%
2306
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02000
66.7%
4352
 
11.7%
3342
 
11.4%
2306
 
10.2%

Glucose_gL
Real number (ℝ)

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.123831
Minimum7.4561285
Maximum32.630807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2026-02-10T14:44:17.598123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.4561285
5-th percentile11.858912
Q115.538837
median18.000308
Q320.752662
95-th percentile24.661587
Maximum32.630807
Range25.174679
Interquartile range (IQR)5.2138252

Descriptive statistics

Standard deviation3.8785262
Coefficient of variation (CV)0.21400146
Kurtosis-0.0073896237
Mean18.123831
Median Absolute Deviation (MAD)2.5948392
Skewness0.10312969
Sum18123.831
Variance15.042966
MonotonicityNot monotonic
2026-02-10T14:44:17.724351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.838180141
 
0.1%
17.263047841
 
0.1%
10.983657751
 
0.1%
13.619575791
 
0.1%
22.296741571
 
0.1%
14.330602941
 
0.1%
20.942097021
 
0.1%
24.633940171
 
0.1%
17.28824731
 
0.1%
20.079453761
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
7.456128531
0.1%
7.774728531
0.1%
7.8023077781
0.1%
8.3078140041
0.1%
8.351544381
0.1%
8.4884978611
0.1%
8.6201184681
0.1%
8.6666086331
0.1%
8.8065318481
0.1%
8.9041991631
0.1%
ValueCountFrequency (%)
32.630807221
0.1%
29.536929111
0.1%
29.064559681
0.1%
28.592575941
0.1%
28.525761621
0.1%
28.284280631
0.1%
28.097586151
0.1%
28.057754551
0.1%
28.015184911
0.1%
27.995469541
0.1%

Biomass_OD600
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9166814
Minimum-3.5954812
Maximum13.468514
Zeros0
Zeros (%)0.0%
Negative61
Negative (%)6.1%
Memory size7.9 KiB
2026-02-10T14:44:17.839164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-3.5954812
5-th percentile-0.27348255
Q12.4058082
median4.8403254
Q37.4536635
95-th percentile10.153515
Maximum13.468514
Range17.063995
Interquartile range (IQR)5.0478553

Descriptive statistics

Standard deviation3.2609359
Coefficient of variation (CV)0.66323922
Kurtosis-0.70755095
Mean4.9166814
Median Absolute Deviation (MAD)2.5187418
Skewness0.0041858517
Sum4916.6814
Variance10.633703
MonotonicityNot monotonic
2026-02-10T14:44:17.954587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8674936091
 
0.1%
0.81180601971
 
0.1%
0.33930860271
 
0.1%
-1.7661861111
 
0.1%
-0.76136917031
 
0.1%
0.41623264881
 
0.1%
-0.19466470631
 
0.1%
2.2393846391
 
0.1%
1.6005548011
 
0.1%
4.9385678811
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-3.5954812111
0.1%
-3.1716659691
0.1%
-3.0227754971
0.1%
-2.8289484241
0.1%
-2.5537227031
0.1%
-2.4942535991
0.1%
-2.3302370361
0.1%
-2.0543525971
0.1%
-2.0336611621
0.1%
-1.7661861111
0.1%
ValueCountFrequency (%)
13.468513521
0.1%
12.993318791
0.1%
12.44990951
0.1%
12.401084851
0.1%
12.279262421
0.1%
12.046102431
0.1%
11.994512241
0.1%
11.732217811
0.1%
11.59858141
0.1%
11.57764491
0.1%

Enzyme_Activity_UmL
Real number (ℝ)

High correlation  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.21001046
Minimum-14.588434
Maximum7.362636
Zeros0
Zeros (%)0.0%
Negative484
Negative (%)48.4%
Memory size7.9 KiB
2026-02-10T14:44:18.078854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-14.588434
5-th percentile-5.9846506
Q1-1.2933915
median0.13397867
Q31.5439589
95-th percentile3.57565
Maximum7.362636
Range21.95107
Interquartile range (IQR)2.8373505

Descriptive statistics

Standard deviation2.945476
Coefficient of variation (CV)-14.025378
Kurtosis4.4107926
Mean-0.21001046
Median Absolute Deviation (MAD)1.4271966
Skewness-1.6189902
Sum-210.01046
Variance8.6758291
MonotonicityNot monotonic
2026-02-10T14:44:18.190239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.66080477051
 
0.1%
-2.1695443311
 
0.1%
-1.7811126941
 
0.1%
-0.80466611381
 
0.1%
0.16985089941
 
0.1%
4.1419739381
 
0.1%
-1.7731121871
 
0.1%
2.7589462111
 
0.1%
-0.55449136331
 
0.1%
-3.7858415811
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
-14.588434451
0.1%
-13.830020741
0.1%
-12.570303281
0.1%
-12.246927261
0.1%
-12.147923971
0.1%
-11.830279021
0.1%
-11.675225161
0.1%
-11.297774691
0.1%
-11.293243721
0.1%
-10.999378291
0.1%
ValueCountFrequency (%)
7.3626359811
0.1%
6.0266008181
0.1%
5.6224722911
0.1%
5.4735484931
0.1%
5.4682475071
0.1%
5.4133735371
0.1%
5.4080853791
0.1%
5.3634997081
0.1%
5.3246233321
0.1%
5.3130343951
0.1%

Batch_ID
Categorical

Uniform 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
Batch_A
200 
Batch_B
200 
Batch_C
200 
Batch_D
200 
Batch_E
200 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBatch_A
2nd rowBatch_A
3rd rowBatch_A
4th rowBatch_A
5th rowBatch_A

Common Values

ValueCountFrequency (%)
Batch_A200
20.0%
Batch_B200
20.0%
Batch_C200
20.0%
Batch_D200
20.0%
Batch_E200
20.0%

Length

2026-02-10T14:44:18.300086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-10T14:44:18.373616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
batch_a200
20.0%
batch_b200
20.0%
batch_c200
20.0%
batch_d200
20.0%
batch_e200
20.0%

Most occurring characters

ValueCountFrequency (%)
B1200
17.1%
a1000
14.3%
t1000
14.3%
c1000
14.3%
h1000
14.3%
_1000
14.3%
A200
 
2.9%
C200
 
2.9%
D200
 
2.9%
E200
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B1200
17.1%
a1000
14.3%
t1000
14.3%
c1000
14.3%
h1000
14.3%
_1000
14.3%
A200
 
2.9%
C200
 
2.9%
D200
 
2.9%
E200
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B1200
17.1%
a1000
14.3%
t1000
14.3%
c1000
14.3%
h1000
14.3%
_1000
14.3%
A200
 
2.9%
C200
 
2.9%
D200
 
2.9%
E200
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)7000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B1200
17.1%
a1000
14.3%
t1000
14.3%
c1000
14.3%
h1000
14.3%
_1000
14.3%
A200
 
2.9%
C200
 
2.9%
D200
 
2.9%
E200
 
2.9%

Batch_Failure
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
987 
0
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1987
98.7%
013
 
1.3%

Length

2026-02-10T14:44:18.480965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-10T14:44:18.546016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1987
98.7%
013
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1987
98.7%
013
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1987
98.7%
013
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1987
98.7%
013
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1987
98.7%
013
 
1.3%

Interactions

2026-02-10T14:44:15.348916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.073239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.725817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.415527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.188615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.918601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.579444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.448019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.166608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.820541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.603182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.288513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.016079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.683529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.541545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.255313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.912412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.701302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.397207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.119892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.786084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.641471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.355227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.011832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.799765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.501909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.215823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.932442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.746995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.454496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.120340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.907564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.613178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.312401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.045136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.834633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.544439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.217814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.996412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.714650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.399500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.142859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.938344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:11.639713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:12.321170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.096810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:13.816891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:14.494623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-10T14:44:15.253062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-10T14:44:18.601215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Agitation_RPMBatch_FailureBatch_IDBiomass_OD600DO_percentEnzyme_Activity_UmLGlucose_gLTemperature_CTime_hrpH
Agitation_RPM1.0000.0040.0000.0360.0000.0270.0000.0660.0300.060
Batch_Failure0.0041.0000.0000.0310.0340.9170.0000.0500.0210.000
Batch_ID0.0000.0001.0000.0060.1390.2170.0000.0000.0000.000
Biomass_OD6000.0360.0310.0061.000-0.102-0.041-0.0110.0050.897-0.025
DO_percent0.0000.0340.139-0.1021.0000.1500.0730.011-0.128-0.006
Enzyme_Activity_UmL0.0270.9170.217-0.0410.1501.000-0.0300.017-0.0190.019
Glucose_gL0.0000.0000.000-0.0110.073-0.0301.0000.001-0.012-0.020
Temperature_C0.0660.0500.0000.0050.0110.0170.0011.0000.012-0.064
Time_hr0.0300.0210.0000.897-0.128-0.019-0.0120.0121.000-0.024
pH0.0600.0000.000-0.025-0.0060.019-0.020-0.064-0.0241.000

Missing values

2026-02-10T14:44:16.073299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-10T14:44:16.189005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Time_hrTemperature_CpHDO_percentAgitation_RPMGlucose_gLBiomass_OD600Enzyme_Activity_UmLBatch_IDBatch_Failure
0037.7450717.07987138.24821730013.8381802.867494-0.660805Batch_A1
1136.7926046.98492743.55481330017.2630480.811806-2.169544Batch_A1
2237.9715336.81192637.07580130010.9836580.339309-1.781113Batch_A1
3339.2845456.67061341.92038520013.619576-1.766186-0.804666Batch_A1
4436.6487706.93964526.06385340022.296742-0.7613690.169851Batch_A1
5536.6487956.87869747.13293740014.3306030.4162334.141974Batch_A1
6639.3688196.97903945.01205530020.942097-0.194665-1.773112Batch_A1
7738.1511526.92703436.82911420024.6339402.2393852.758946Batch_A1
8836.2957887.00991151.59245740017.2882471.600555-0.554491Batch_A1
9937.8138406.69295354.37570130020.0794544.938568-3.785842Batch_A1
Time_hrTemperature_CpHDO_percentAgitation_RPMGlucose_gLBiomass_OD600Enzyme_Activity_UmLBatch_IDBatch_Failure
9909037.3125746.96008260.16394140010.4836158.673875-1.367545Batch_E1
9919133.9373986.95085851.02118330021.72383710.6464351.848706Batch_E1
9929236.6292347.03778345.72036930017.89885110.116627-0.255147Batch_E1
9939335.9770246.94166142.87791040023.1039099.735302-1.146830Batch_E1
9949435.4975706.87029035.48081520011.3387648.9929490.426085Batch_E1
9959536.5783507.01403045.77480530016.83391511.164966-2.395440Batch_E1
9969639.6965306.79469647.57752540017.13438212.4010852.482667Batch_E1
9979737.9612646.62362532.58239440019.08082511.2134690.449432Batch_E1
9989836.1432326.76738748.34176430012.6992519.1047030.387744Batch_E1
9999937.8588746.65101943.44741020020.17615111.0723480.379284Batch_E1